Computer Science

Research shows that people with low incomes and racial/ethnic minority populations experience greater levels of stress than their more affluent, white counterparts. This can lead to significant disparities in both mental and physical health that ultimately affect life expectancy and shows correlation with a reduction in household SES as paired with mental and physical health barriers. To learn more about ... Read More

We present an architecture for learning semantic multi-modal video representations to learn semantic representations of videos from unlabeled data with transformer architectures. While multi-modal transformer architectures have been shown to increase accuracy of video classification and feature learning tasks, these techniques do not incorporate semantic information. Our Semantic Contrastive Multi-Modal Video Transformer (SCMMVT) takes raw video, audio, and text data ... Read More

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to

the data) and filters (data splits according to ... Read More

Artificial Intelligence-based analysis techniques have struggled to make headway in the field of neurological analysis. However, the systems that have been successfully implemented have shaped our modern understanding of neurological interactions. These analysis techniques are largely limited by a lack of robust frameworks for data generation and application, especially with relation to applications that require the usage of large datasets. ... Read More

Idiopathic Pulmonary Fibrosis (IPF) is a lung disease with a mean survival of 2-5 years from the time of diagnosis in which alveolar tissue progressively becomes stiff fibrotic scar tissue, reducing breathing capacity and eventually leading to respiratory failure. The use of Machine Learning could predict IPF cases more precisely than the current surgical lung biopsy treatment years before the ... Read More

The elderly population is disproportionately affected by Alzheimer’s disease, which is observed cognitively. Using a set of next-generation RNA sequencing project (ROSMAP) from NorthShore University HealthSystem combined with clinical diagnostic profiles, we constructed a neural network-based deep learning model to predict the occurrence and severity of three geriatric-related pathological features. The final consensus cognitive diagnosis (cogdx) was taken post-mortem. Braak ... Read More

In the modern day, Artificial Neural Networks (ANNs) have been recently used and tested to calculate and predict options pricing [Ke 2019], but currently, the most tested and reliable methods are the Binomial and Trinomial methods. Binomial and trinomial pricing models are valuation methods for financial derivatives, namely, options. The methods create a binomial or trinomial tree through an iterative ... Read More